diff --git a/Case Study 6 Social Network.ipynb b/Case Study 6 Social Network.ipynb new file mode 100644 index 0000000..97c82da --- /dev/null +++ b/Case Study 6 Social Network.ipynb @@ -0,0 +1,1733 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3.1: Introduction to Network Analysis\n", + "Learn about the basic components of networks and the graphs that represent them\n", + "\n", + "Learn basic network concepts such as neighbor, degree, path, component, and largest connected component" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many systems of scientific and societal interest consist of a large number of interacting components.\n", + "\n", + "The structure of these systems can be represented as networks where network **nodes** represent the components,\n", + "and network **edges,** the interactions between the components.\n", + "\n", + "This case study first introduces some basic concepts about networks, we'll then write a Python function to generate very simple random graphs, and finally, we'll analyze some basic properties of social networks collected\n", + "in different rural villages in India.\n", + "\n", + "Basic network concepts: **neighbor, degree, path, component, largest connected component**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3.2: Basics of NetworkX\n", + "Learn how to use the NetworkX module to create and manipulate network graphs.\n", + "\n", + "Networks are created and manipulated using the NetworkX module." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# We can create an instance of an undirected graph using that Graph function.\n", + "G = nx.Graph()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NodeView((1, 2, 3, 'u', 'v'))" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can now add nodes one at a time or several at a time.\n", + "G.add_node(1)\n", + "G.add_nodes_from([2,3]) # nodi multipli in un list\n", + "G.add_nodes_from([\"u\",\"v\"]) # possiamo usare anche stringhe\n", + "\n", + "# We can also get a list of the current nodes.\n", + "G.nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Similar functions exist also for adding edges. Remember that edges are treated as pairs of nodes.\n", + "G.add_edge(1,2)\n", + "G.add_edge(\"u\",\"v\")\n", + "G.add_edges_from([(1,3),(1,4),(1,5),(1,6)]) # una list di tuple, anche se i nodi non c'erano, \n", + " # vengono aggiunti automaticamente\n", + "G.add_edge(\"u\",\"w\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EdgeView([(1, 2), (1, 3), (1, 4), (1, 5), (1, 6), ('u', 'v'), ('u', 'w')])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# possiamo visualizzare gli edges\n", + "G.edges() " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# rimuovere un nodo:\n", + "G.remove_node(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NodeView((1, 3, 'u', 'v', 4, 5, 6, 'w'))" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EdgeView([(1, 3), (1, 4), (1, 5), (1, 6), ('u', 'v'), ('u', 'w')])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.edges() # vengono rimossi anche gli edges del nodo rimosso" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# rimuovere nodi multipli\n", + "G.remove_nodes_from([4,5])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NodeView((1, 3, 'u', 'v', 6, 'w'))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EdgeView([(1, 3), (1, 6), ('u', 'v'), ('u', 'w')])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.edges()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EdgeView([(1, 6), ('u', 'v'), ('u', 'w')])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rimuovere gli edges\n", + "G.remove_edge(1, 3) \n", + "G.edges()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# rimuovere più di un edge\n", + "G.remove_edges_from([(1, 6),(1, 'u')])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EdgeView([('u', 'v'), ('u', 'w')])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.edges()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n", + "2\n" + ] + } + ], + "source": [ + "# conteggi\n", + "print(G.number_of_nodes())\n", + "print(G.number_of_edges())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3.3: Graph Visualization\n", + "How to use networkx to visualize a graph.\n", + "\n", + "Networkx contains many types of random graph generators.\n", + "But in addition, it also contains a few **empirical data sets**.\n", + "Let's use one of them called the **karate club graph**.\n", + "In this network, the nodes represent members of a karate club and the edges\n", + "correspond to friendships between the members." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "G = nx.karate_club_graph()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "nx.draw(G, with_labels=True, node_color=\"lightblue\", edge_color=\"grey\" )\n", + "plt.savefig(\"karate_graph.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DegreeView({0: 16, 1: 9, 2: 10, 3: 6, 4: 3, 5: 4, 6: 4, 7: 4, 8: 5, 9: 2, 10: 3, 11: 1, 12: 2, 13: 5, 14: 2, 15: 2, 16: 2, 17: 2, 18: 2, 19: 3, 20: 2, 21: 2, 22: 2, 23: 5, 24: 3, 25: 3, 26: 2, 27: 4, 28: 3, 29: 4, 30: 4, 31: 6, 32: 12, 33: 17})" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Networkx stores the degrees of nodes in a DegreeView object where the \n", + "# keys are node IDs and the values are their associated degrees.\n", + "G.degree()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# grado di un nodo:\n", + "G.degree()[3] # Come un dict!!!" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# possiamo anche scrivere così: \n", + "G.degree(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.number_of_nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "78" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.number_of_edges()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.degree(0) is G.degree()[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3.4: Random Graphs\n", + "The simplest possible random graph model is the so-called **Erdos-Renyi, also known as the ER graph model.**\n", + "Questa famiglia di grafi random ha due parametri, **N**, numero di nodi e **p**, probabilità di ogni coppia di nodi di essere connesso da un edge.\n", + "\n", + "**Although the NetworkX library includes an Erdos-Renyi graph generator, we'll be writing our own ER function to better understand the model.**\n", + "\n", + "Our task is to implement an ER model as a Python function. \n", + "Let's first see how to implement the coin flip just one time.\n", + "To do this, we'll be using the SciPy stats module, more specifically a function called Bernoulli.\n", + "We'll first import that from SciPy stats import Bernoulli." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import bernoulli\n", + "# una semplice funzione di probabilità che ritorna 0 o 1\n", + "bernoulli.rvs(p = 0.2) # p = probabilità di successo (che venga 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Utilizziamo la precedente per creare un grafo \n", + "# che ha un certo numero di nodi e una certa prob. che una coppia di nodi sia connessa:\n", + "N = 20 # Numero di nodi\n", + "p = 0.2 # probabilità che due nodi siano collegati\n", + "\n", + "# creare un grafo vuoto\n", + "G = nx.Graph()\n", + "# popolarlo coi nodi\n", + "G.add_nodes_from(range(N))\n", + "# fare un loop tra tutte le coppie di nodi\n", + "for node1 in G.nodes():\n", + " for node2 in G.nodes(): # ATTENZIONE: COSI' CONTIAMO OGNI COPPIA DUE VOLTE!!! Quindi vedi riga seguente\n", + " if node1 < node2 and bernoulli.rvs(p=p):\n", + " G.add_edge(node1, node2) # aggiunge un edge con probabilità p\n", + "\n", + "# disegnamo il grafo che abbiamo creato:\n", + "nx.draw(G, with_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Possiamo infine creare una function:\n", + "def er_graph(N, p):\n", + " '''Generate an ER graph'''\n", + " G = nx.Graph()\n", + " G.add_nodes_from(range(N))\n", + " for node1 in G.nodes():\n", + " for node2 in G.nodes(): \n", + " if node1 < node2 and bernoulli.rvs(p=p):\n", + " G.add_edge(node1, node2)\n", + " return G" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = er_graph(50, 0.08)\n", + "nx.draw(G, with_labels = True, node_size = 40, node_color = \"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3.5: Plotting the Degree Distribution\n", + "Creiamo una function che fa il plot della distribuzione dei gradi" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "networkx.classes.reportviews.DegreeView" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(G.degree()) # non è più, come nelle versioni precedenti di networkx, un dictionary.\n", + "# E' però simile e si può trasformare." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_values([4, 3, 2, 3, 3, 4, 7, 4, 3, 5, 1, 2, 3, 4, 5, 4, 3, 3, 6, 3, 5, 3, 3, 5, 4, 3, 4, 2, 4, 5, 8, 5, 6, 2, 3, 3, 2, 5, 3, 5, 5, 2, 5, 3, 6, 6, 5, 4, 8, 8])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict(G.degree()).values()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4,\n", + " 3,\n", + " 2,\n", + " 3,\n", + " 3,\n", + " 4,\n", + " 7,\n", + " 4,\n", + " 3,\n", + " 5,\n", + " 1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 4,\n", + " 3,\n", + " 3,\n", + " 6,\n", + " 3,\n", + " 5,\n", + " 3,\n", + " 3,\n", + " 5,\n", + " 4,\n", + " 3,\n", + " 4,\n", + " 2,\n", + " 4,\n", + " 5,\n", + " 8,\n", + " 5,\n", + " 6,\n", + " 2,\n", + " 3,\n", + " 3,\n", + " 2,\n", + " 5,\n", + " 3,\n", + " 5,\n", + " 5,\n", + " 2,\n", + " 5,\n", + " 3,\n", + " 6,\n", + " 6,\n", + " 5,\n", + " 4,\n", + " 8,\n", + " 8]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# per avere i gradi, è più corretto utilizzare la seguente:\n", + "[d for n, d in G.degree()]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# possiamo definire la funzione:\n", + "def plot_degree_distribution(G):\n", + " '''plot della distribuzione dei gradi di un grafo'''\n", + " degree_sequence = [d for n, d in G.degree()]\n", + " plt.hist(degree_sequence, histtype=\"step\")\n", + " plt.xlabel(\"Degree $k$\")\n", + " plt.ylabel(\"$P(k)$\")\n", + " plt.title(\"Degree distribution\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = er_graph(50,0.08)\n", + "plot_degree_distribution(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Aumentiamo il numero di nodi e facciamo tre grafi\n", + "G1 = er_graph(500,0.08)\n", + "plot_degree_distribution(G1)\n", + "G2 = er_graph(500,0.08)\n", + "plot_degree_distribution(G2)\n", + "G3 = er_graph(500,0.08)\n", + "plot_degree_distribution(G3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3.6: Descriptive Statistics of Empirical Social Networks\n", + "* Look at the basic properties of social networks in two villages in rural India\n", + "* Compare the degree distribution of these empirical networks with the degree distribution of the ER networks\n", + "\n", + "In this task, we will look at basic properties of the social networks from two different villages in rural India.\n", + "These data are part of a much larger dataset that was collected to study diffusion of micro-finance. And the findings of this study were published in an article called, \"The Diffusion of Micro-finance,\" in the Journal Science\n", + "in 2013." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The structure of connections in a network can be captured in the **Adjacency matrix** of the network. \n", + "Se i nodi sono n, è una **matrice n x n. Il valore ij è 1 se i j sono collegati (quindi è simmetrica).**\n", + "\n", + "Abbiamo le adjacency matrix come files CSV, uno per ciascun villaggio. \n", + "Leggiamo le adjacency matrix e costruiamo le reti tramite il metodo **to_networkx_graph()**." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "A1 = np.loadtxt(\"adj_allVillageRelationships_vilno_1.csv\", delimiter = \",\") # Adjacency matrix 1\n", + "A2 = np.loadtxt(\"adj_allVillageRelationships_vilno_2.csv\", delimiter = \",\") # Adjacency matrix 2\n", + "G1 = nx.to_networkx_graph(A1)\n", + "G2 = nx.to_networkx_graph(A2)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# To get a basic sense of the network size and number of connections, \n", + "# let's count the number of nodes and the number of edges in the networks.\n", + "# In addition, each node has a total number of edges, its degree.\n", + "# Let's also calculate the mean degree for all nodes in the network." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "def basic_net_stats(G):\n", + " print(f\"Number of nodes: {G.number_of_nodes()}\")\n", + " print(f\"Number of edges: {G.number_of_edges()}\")\n", + " degree_sequence = [d for n, d in G.degree()]\n", + " print(\"Average degree: %.2f\" %np.mean(degree_sequence))" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of nodes: 843\n", + "Number of edges: 3405\n", + "Average degree: 8.08\n", + "Number of nodes: 877\n", + "Number of edges: 3063\n", + "Average degree: 6.99\n" + ] + } + ], + "source": [ + "basic_net_stats(G1)\n", + "\n", + "basic_net_stats(G2)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_degree_distribution(G1)\n", + "plot_degree_distribution(G2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems that most people have relatively few connections,\n", + "whereas a small fraction of people have a large number of connections.\n", + "This distribution doesn't look at all symmetric,\n", + "and its tail extends quite far to the right.\n", + "\n", + "This suggests that the ER graphs are likely not good models\n", + "for real world social networks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3.7: Finding the Largest Connected Component\n", + "\n", + "- Learn how to find the largest connected component in a network\n", + "- Learn how to visualize the largest connected component" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Molte reti sono definite da un singolo componente o in ogni caso da un componente più grande, \n", + "# in cui ogni singolo nodo è connesso a tutti gli altri.\n", + "# Quindi, per ciascuna coppia di nodi, esiste un percorso tra loro.\n", + "# C'è una funzione per estrarre il componente più grande:\n", + "nx.connected_component_subgraphs(G1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Come vediamo, il risultato è un **generator object**.\n", + "Le **generator function** non ritornano un oggetto singolo, invece sono utilizzate per **generare una sequenza di oggetti**.\n", + "\n", + "Possiamo creare un generatore:" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "gen = nx.connected_component_subgraphs(G1)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "# Utilizziamo il metodo __next__:\n", + "g = gen.__next__()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "networkx.classes.graph.Graph" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "825" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g.number_of_nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# possiamo usare anche la len() Ogni volta ha un diverso risultato, perché andiamo al prossimo (next)\n", + "len(gen.__next__())" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "# Possiamo usare un'altra funzione che chiama implicitamente il metodo next\n", + "# Possiamo usare la max function per ottenere il massimo della sequenza:\n", + "G1_LCC = max(nx.connected_component_subgraphs(G1), key = len) # LCC per Largest Connected Component\n", + "G2_LCC = max(nx.connected_component_subgraphs(G2), key = len)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "825" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(G1_LCC)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "843" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(G1)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "810" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(G2_LCC)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "877" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(G2)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9786476868327402" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Possiamo calcolare il rapporto tra i LCC e il numero totale dei nodi del grafo:\n", + "# caso 1:\n", + "\n", + "len(G1_LCC)/len(G1)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9236031927023945" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Possiamo fare lo stesso per G2, (qui usiamo la number_of_nodes, che è equivalente a len)\n", + "G2_LCC.number_of_nodes() / G2.number_of_nodes()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "it is very common for networks to contain one component that encompasses a large majority of its nodes, 95, 99, or even 99.9% of all of the nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Proviamo a fare la rappresentazione grafica.\n", + "plt.figure()\n", + "nx.draw(G1_LCC, node_color = \"red\", node_edge = \"grey\", node_size = 20)\n", + "plt.savefig(\"village1.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# lo stesso per l'altro villaggio\n", + "plt.figure()\n", + "nx.draw(G2_LCC, node_color = \"green\", node_edge = \"grey\", node_size = 20)\n", + "plt.savefig(\"village2.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In most visualizations (they are stochastics, so can change every time), you should find that the largest connected component of G2\n", + "appears to consist of two separate groups. These groups are called **network communities**. \n", + "\n", + "A community is a group of nodes that are densely connected to other nodes in the group, but only sparsely connected nodes outside of that group." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Homework: Case Study 6\n", + "**Homophily** is a network characteristic. **Homophily occurs when nodes that are neighbors in a network also share a characteristic more often than nodes that are not network neighbors**. In this case study, we will investigate homophily of several characteristics of individuals connected in social networks in rural India.\n", + "\n", + "In this exercise, **we will calculate the chance homophily for an arbitrary characteristic**. Homophily is the **proportion of edges in the network whose constituent nodes share that characteristic**. How much homophily do we expect by chance? If characteristics are distributed completely randomly, the probability that two nodes x and y share characteristic a is the probability both nodes have characteristic a, which is the frequency of a squared. **The total probability that nodes x and y share their characteristic is therefore the sum of the frequency of each characteristic in the network**. For **example, in the dictionary favorite_colors** provided, **the frequency of red and blue is 1/3 and 2/3 respectively, so the chance homophily is (1/3)^2+(2/3)^2 = 5/9.**\n", + "\n", + "### Exercise 1\n", + "- Create a function that takes a dictionary *chars* with personal IDs as keys and characteristics as values, and returns a dictionary with characteristics as keys, and the frequency of their occurrence as values.\n", + "- Create a function chance_homophily(chars) that takes a dictionary chars defined as above and computes the chance homophily for that characteristic.\n", + "- A sample of three peoples' favorite colors is given in favorite_colors. Use your function to compute the chance homophily in this group, and store as color_homophily.\n", + "- Print color_homophily." + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "def frequency(chars):\n", + " '''Take a dictionary \"chars\" with personal IDs as keys and characteristics as values, \n", + " and returns a dictionary with characteristics as keys and the frequency of their occurrence as values'''\n", + " frequency = {}\n", + " for key in chars:\n", + " if chars[key] in frequency.keys():\n", + " frequency[chars[key]] += 1\n", + " else:\n", + " frequency[chars[key]] = 1\n", + " return frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'red': 2, 'blue': 1, 'green': 1, 'pink': 1}\n" + ] + } + ], + "source": [ + "dict = {\"id1\":\"red\",\"id2\":\"blue\",\"id3\":\"red\",\"id4\":\"green\",\"id5\": \"pink\"}\n", + "chars = frequency(dict)\n", + "print(chars)\n", + "#a = np.sum(list(chars.values()))\n", + "#a" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.625" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def chance_homophily(chars):\n", + " freq = frequency(chars)\n", + " chance_homophily = 0\n", + " tot_occurr = np.sum(list(freq.values()))\n", + " for key in freq:\n", + " chance_homophily += np.square(freq[key] / tot_occurr)\n", + " return chance_homophily\n", + "\n", + "chance_homophily(chars) \n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5555555555555556\n" + ] + } + ], + "source": [ + "favorite_colors = {\n", + " \"ankit\": \"red\",\n", + " \"xiaoyu\": \"blue\",\n", + " \"mary\": \"blue\"\n", + "}\n", + "\n", + "color_homophily = chance_homophily(favorite_colors)\n", + "print(color_homophily)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2\n", + "- Individual_characteristics.dta contains several characteristics for each individual in the dataset such as age, religion, and caste. Use the pandas library to read in and store these characteristics as a dataframe called df.\n", + "- Store separate datasets for individuals belonging to Villages 1 and 2 as df1 and df2, respectively. Note that some attributes may be missing for some individuals. In this case study, we will ignore rows of data where some column information is missing.\n", + "- Use the head method to display the first few entries of df1." + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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villageadjmatrix_keypidhhidresp_idresp_gendresp_statusagereligioncaste...privategovtwork_outsidework_outside_freqshgparticipateshg_nosavingssavings_noelectioncardrationcardrationcard_colour
015100201100211Head of Household38HINDUISMOBC...PRIVATE BUSINESSYes0NoNaNNoNaNYesYesGREEN
116100202100222Spouse of Head of Household27HINDUISMOBC...NaNNaNNoNaNNoNaNYesYesGREEN
2123100601100611Head of Household29HINDUISMOBC...OTHER LANDNoNaNNoNaNNoNaNYesYesGREEN
3124100602100622Spouse of Head of Household24HINDUISMOBC...PRIVATE BUSINESSNoNaNYes1Yes1.0YesNo
4127100701100711Head of Household58HINDUISMOBC...OTHER LANDNoNaNNoNaNNoNaNYesYesGREEN
\n", + "

5 rows × 48 columns

\n", + "
" + ], + "text/plain": [ + " village adjmatrix_key pid hhid resp_id resp_gend \\\n", + "0 1 5 100201 1002 1 1 \n", + "1 1 6 100202 1002 2 2 \n", + "2 1 23 100601 1006 1 1 \n", + "3 1 24 100602 1006 2 2 \n", + "4 1 27 100701 1007 1 1 \n", + "\n", + " resp_status age religion caste ... \\\n", + "0 Head of Household 38 HINDUISM OBC ... \n", + "1 Spouse of Head of Household 27 HINDUISM OBC ... \n", + "2 Head of Household 29 HINDUISM OBC ... \n", + "3 Spouse of Head of Household 24 HINDUISM OBC ... \n", + "4 Head of Household 58 HINDUISM OBC ... \n", + "\n", + " privategovt work_outside work_outside_freq shgparticipate shg_no \\\n", + "0 PRIVATE BUSINESS Yes 0 No NaN \n", + "1 NaN NaN No NaN \n", + "2 OTHER LAND No NaN No NaN \n", + "3 PRIVATE BUSINESS No NaN Yes 1 \n", + "4 OTHER LAND No NaN No NaN \n", + "\n", + " savings savings_no electioncard rationcard rationcard_colour \n", + "0 No NaN Yes Yes GREEN \n", + "1 No NaN Yes Yes GREEN \n", + "2 No NaN Yes Yes GREEN \n", + "3 Yes 1.0 Yes No \n", + "4 No NaN Yes Yes GREEN \n", + "\n", + "[5 rows x 48 columns]" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_stata(\"individual_characteristics.dta\")\n", + "\n", + "df1 = df[df.village == 1]\n", + "df2 = df[df.village == 2]\n", + "\n", + "df.head()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 3\n", + "- Define dictionaries with personal IDs as keys and a given covariate for that individual as values. Complete this for the sex, caste, and religion covariates, for Villages 1 and 2.\n", + "- For Village 1, store these dictionaries into variables named sex1, caste1, and religion1.\n", + "- For Village 2, store these dictionaries into variables named sex2, caste2, and religion2." + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "sex1 = df1.set_index(\"pid\")[\"resp_gend\"].to_dict()\n", + "caste1 = df1.set_index(\"pid\")[\"caste\"].to_dict()\n", + "religion1 = df1.set_index(\"pid\")[\"religion\"].to_dict()\n", + "\n", + "sex2 = df2.set_index(\"pid\")[\"resp_gend\"].to_dict()\n", + "caste2 = df2.set_index(\"pid\")[\"caste\"].to_dict()\n", + "religion2 = df2.set_index(\"pid\")[\"religion\"].to_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 4\n", + "sex1, caste1, religion1, sex2, caste2, and religion2 are already defined from previous exercises. Use chance_homophily to compute the chance homophily for sex, caste, and religion In Villages 1 and 2. Is the chance homophily for any attribute very high for either village?" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Village 1 chance of same sex: 0.5027299861680701\n", + "Village 1 chance of same caste: 0.6741488509791551\n", + "Village 1 chance of same religion: 0.9804896988521925\n", + "Village 2 chance of same sex: 0.5005945303210464\n", + "Village 2 chance of same caste: 0.425368244800893\n", + "Village 2 chance of same religion: 1.0\n" + ] + } + ], + "source": [ + "print(\"Village 1 chance of same sex:\", chance_homophily(sex1))\n", + "# Enter your code here.\n", + "print(\"Village 1 chance of same caste:\", chance_homophily(caste1))\n", + "print(\"Village 1 chance of same religion:\", chance_homophily(religion1))\n", + "print(\"Village 2 chance of same sex:\", chance_homophily(sex2))\n", + "print(\"Village 2 chance of same caste:\", chance_homophily(caste2))\n", + "print(\"Village 2 chance of same religion:\", chance_homophily(religion2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 5\n", + "Complete the function homophily(), which takes a network G, a dictionary of characteristics chars, and node IDs IDs. For each node pair, determine whether a tie exists between them, as well as whether they share a characteristic. The total count of these is num_same_ties and num_ties respectively, and their ratio is the homophily of chars in G. Complete the function by choosing where to increment num_same_ties and num_ties." + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [], + "source": [ + "def homophily(G, chars, IDs):\n", + " \"\"\"\n", + " Given a network G, a dict of characteristics chars for node IDs,\n", + " and dict of node IDs for each node in the network,\n", + " find the homophily of the network.\n", + " \"\"\"\n", + " num_same_ties = 0\n", + " num_ties = 0\n", + " for n1, n2 in G.edges():\n", + " if n1 > n2: # do not double-count edges!\n", + " if IDs[n1] in chars and IDs[n2] in chars:\n", + " \n", + " if G.has_edge(n1, n2):\n", + " num_ties += 1\n", + " if chars[IDs[n1]] == chars[IDs[n2]]:\n", + " num_same_ties += 1\n", + " if num_ties != 0:\n", + " return (num_same_ties / num_ties)\n", + " else:\n", + " return 0\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 6\n", + "In this dataset, each individual has a personal ID, or PID, stored in key_vilno_1.csv and key_vilno_2.csv for villages 1 and 2, respectively. data_filepath contains the base URL to the datasets used in this exercise. Use pd.read_csv to read in and store key_vilno_1.csv and key_vilno_2.csv as pid1 and pid2 respectively. The csv files have no headers, so make sure to include the parameter header = None." + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [], + "source": [ + "data_filepath = \"\"\n", + "pid1 = pd.read_csv(data_filepath + \"adj_allVillageRelationships_vilno_2.csv\", dtype=int, header = None)\n", + "pid2 = pd.read_csv(data_filepath + \"adj_allVillageRelationships_vilno_1.csv\", dtype=int, header = None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 7\n", + "Use your homophily() function to compute the observed homophily for sex, caste, and religion in Villages 1 and 2. Print all six values.\n", + "Use the chance_homophily() to compare these values to chance homophily. Are these values higher or lower than that expected by chance?" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Village 1 observed proportion of same sex: 0\n", + "Village 1 observed proportion of same caste: 0\n", + "Village 1 observed proportion of same religion: 0\n", + "Village 2 observed proportion of same sex: 0\n", + "Village 2 observed proportion of same caste: 0\n", + "Village 2 observed proportion of same religion: 0\n" + ] + } + ], + "source": [ + "print(\"Village 1 observed proportion of same sex:\", homophily(G1, sex1, pid1))\n", + "print(\"Village 1 observed proportion of same caste:\", homophily(G1, caste1, pid1))\n", + "print(\"Village 1 observed proportion of same religion:\", homophily(G1, religion1, pid1))\n", + "\n", + "print(\"Village 2 observed proportion of same sex:\", homophily(G2, sex2, pid2))\n", + "print(\"Village 2 observed proportion of same caste:\", homophily(G2, caste2, pid2))\n", + "print(\"Village 2 observed proportion of same religion:\", homophily(G2, religion2, pid2))\n", + "\n", + "print(\"Village 1 chance of same sex:\", chance_homophily(sex1))\n", + "print(\"Village 1 chance of same caste:\", chance_homophily(caste1))\n", + "print(\"Village 1 chance of same religion:\", chance_homophily(religion1))\n", + "\n", + "print(\"Village 2 chance of same sex:\", chance_homophily(sex2))\n", + "print(\"Village 2 chance of same caste:\", chance_homophily(caste2))\n", + "print(\"Village 2 chance of same religion:\", chance_homophily(religion2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/README.md b/README.md index 7315e47..0df2c1e 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Using Python for Research -My solutions to homework assignments for Harvardx: PH526x Using Python for Research, EdX, Jul-Aug 2017. All codes are written in Python 3. +My notes for Harvardx: PH526x Using Python for Research, EdX, Sept-Oct 2018. All codes are written in Python 3. -Course Modules: https://www.edx.org/course/using-python-research-harvardx-ph526x +Course Modules: https://www.edx.org/course/using-python-for-research diff --git a/Week1.txt b/Week1.txt new file mode 100644 index 0000000..32af76d --- /dev/null +++ b/Week1.txt @@ -0,0 +1 @@ +This is my first week notes